Yahoo Finance stock price analysis
Apple Inc.
Introduction
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Data
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| Date | AAPL.High | AAPL.Close | AAPL.Volume |
|---|---|---|---|
| 2021-03-08 | 121.00 | 116.36 | 154376600 |
| 2021-03-09 | 122.06 | 121.09 | 129525800 |
| 2021-03-10 | 122.17 | 119.98 | 111943300 |
| 2021-03-11 | 123.21 | 121.96 | 103026500 |
| 2021-03-12 | 121.17 | 121.03 | 88105100 |
| 2021-03-15 | 124.00 | 123.99 | 92403800 |
| Date | AAPL.High | AAPL.Close | AAPL.Volume |
|---|---|---|---|
| 2018-08-22 | 54.0900 | 53.7625 | 76072400 |
| 2018-08-23 | 54.2625 | 53.8725 | 75532800 |
| 2018-08-24 | 54.2250 | 54.0400 | 73905600 |
| 2018-08-27 | 54.6850 | 54.4850 | 82100400 |
| 2018-08-28 | 55.1350 | 54.9250 | 91107200 |
| 2018-08-29 | 55.8725 | 55.7450 | 109019200 |
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Plots
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Plotly interactive graph
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Each on separate tab
AAPL.High
AAPL.Close
AAPL.Volume
Descriptive statistics
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Stationarity analysis
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Trend
##
## ###############################################
## # Augmented Dickey-Fuller Test Unit Root Test #
## ###############################################
##
## Test regression trend
##
##
## Call:
## lm(formula = z.diff ~ z.lag.1 + 1 + tt + z.diff.lag)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.4482 -0.6745 0.0200 0.6234 9.8762
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0168357 0.1283085 -0.131 0.896
## z.lag.1 -0.9476991 0.0531731 -17.823 <2e-16 ***
## tt 0.0003739 0.0003460 1.081 0.280
## z.diff.lag 0.0521255 0.0397233 1.312 0.190
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.619 on 637 degrees of freedom
## Multiple R-squared: 0.4509, Adjusted R-squared: 0.4484
## F-statistic: 174.4 on 3 and 637 DF, p-value: < 2.2e-16
##
##
## Value of test-statistic is: -17.8229 105.8876 158.8303
##
## Critical values for test statistics:
## 1pct 5pct 10pct
## tau3 -3.96 -3.41 -3.12
## phi2 6.09 4.68 4.03
## phi3 8.27 6.25 5.34According to the ADF test the series are integrated of order 1, or in other words have become stationary after 1 differentiation(s).
Drift
##
## ###############################################
## # Augmented Dickey-Fuller Test Unit Root Test #
## ###############################################
##
## Test regression drift
##
##
## Call:
## lm(formula = z.diff ~ z.lag.1 + 1 + z.diff.lag)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.3886 -0.6868 -0.0201 0.5965 9.9354
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.10323 0.06418 1.608 0.108
## z.lag.1 -0.94445 0.05309 -17.788 <2e-16 ***
## z.diff.lag 0.05041 0.03970 1.270 0.205
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.619 on 638 degrees of freedom
## Multiple R-squared: 0.4499, Adjusted R-squared: 0.4482
## F-statistic: 260.9 on 2 and 638 DF, p-value: < 2.2e-16
##
##
## Value of test-statistic is: -17.7879 158.206
##
## Critical values for test statistics:
## 1pct 5pct 10pct
## tau2 -3.43 -2.86 -2.57
## phi1 6.43 4.59 3.78According to the ADF test the series are integrated of order 1, or in other words have become stationary after 1 differentiation(s).
None
##
## ###############################################
## # Augmented Dickey-Fuller Test Unit Root Test #
## ###############################################
##
## Test regression none
##
##
## Call:
## lm(formula = z.diff ~ z.lag.1 - 1 + z.diff.lag)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.3116 -0.5846 0.0810 0.6913 10.0321
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## z.lag.1 -0.93695 0.05296 -17.693 <2e-16 ***
## z.diff.lag 0.04659 0.03967 1.174 0.241
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.621 on 639 degrees of freedom
## Multiple R-squared: 0.4477, Adjusted R-squared: 0.446
## F-statistic: 259 on 2 and 639 DF, p-value: < 2.2e-16
##
##
## Value of test-statistic is: -17.6932
##
## Critical values for test statistics:
## 1pct 5pct 10pct
## tau1 -2.58 -1.95 -1.62According to the ADF test the series are integrated of order 1, or in other words have become stationary after 1 differentiation(s).
Forecasting
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